InChI Tag: Classroom Material

Abstract

Background:

The Blue Obelisk movement was established in 2005 as a response to the lack of Open Data, Open Standards and Open Source (ODOSOS) in chemistry. It aims to make it easier to carry out chemistry research by promoting interoperability between chemistry software, encouraging cooperation between Open Source developers, and developing community resources and Open Standards.

Results:

This contribution looks back on the work carried out by the Blue Obelisk in the past 5 years and surveys progress and remaining challenges in the areas of Open Data, Open Standards, and Open Source in chemistry.

Conclusions:

We show that the Blue Obelisk has been very successful in bringing together researchers and developers with common interests in ODOSOS, leading to development of many useful resources freely available to the chemistry community.

Abstract

UniChem is a freely available compound identifier mapping service on the internet, designed to optimize the efficiency with which structure-based hyperlinks may be built and maintained between chemistry-based resources. In the past, the creation and maintenance of such links at EMBL-EBI, where several chemistry-based resources exist, has required independent efforts by each of the separate teams. These efforts were complicated by the different data models, release schedules, and differing business rules for compound normalization and identifier nomenclature that exist across the organization. UniChem, a large-scale, non-redundant database of Standard InChIs with pointers between these structures and chemical identifiers from all the separate chemistry resources, was developed as a means of efficiently sharing the maintenance overhead of creating these links. Thus, for each source represented in UniChem, all links to and from all other sources are automatically calculated and immediately available for all to use. Updated mappings are immediately available upon loading of new data releases from the sources. Web services in UniChem provide users with a single simple automatable mechanism for maintaining all links from their resource to all other sources represented in UniChem. In addition, functionality to track changes in identifier usage allows users to monitor which identifiers are current, and which are obsolete. Lastly, UniChem has been deliberately designed to allow additional resources to be included with minimal effort. Indeed, the recent inclusion of data sources external to EMBL-EBI has provided a simple means of providing users with an even wider selection of resources with which to link to, all at no extra cost, while at the same time providing a simple mechanism for external resources to link to all EMBL-EBI chemistry resources.

Abstract

The Reaction InChI (RInChI) extends the idea of the InChI, which provides a unique descriptor of molecular structures, towards reactions. Prototype versions of the RInChI have been available since 2011. The frst ofcial release (RInChIV1.00), funded by the InChI Trust, is now available for download (https://www.inchi-trust.org/wp/downloads/). This release defnes the format and generates hashed representations (RInChIKeys) suitable for database and web operations. The RInChI provides a concise description of the key data in chemical processes, and facilitates the manipulation and analysis of reaction data.

Abstract

Background:

An important step in the reconstruction of a metabolic network is annotation of metabolites. Metabolites are generally annotated with various database or structure based identifiers. Metabolite annotations in metabolic reconstructions may be incorrect or incomplete and thus need to be updated prior to their use.
Genome-scale metabolic reconstructions generally include hundreds of metabolites. Manually updating annotations is therefore highly laborious. This prompted us to look for open-source software applications that could facilitate automatic updating of annotations by mapping between available metabolite identifiers. We identified three applications developed for the metabolomics and chemical informatics communities as potential solutions. The applications were MetMask, the Chemical Translation System, and UniChem. The first implements a “metabolite masking” strategy for mapping between identifiers whereas the latter two implement different versions of an InChI based strategy. Here we evaluated the suitability of these applications for the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We applied the best suited application to updating identifiers in Recon 2, the latest reconstruction of human metabolism.

Results:

All three applications enabled partially automatic updating of metabolite identifiers, but significant manual effort was still required to fully update identifiers. We were able to reduce this manual effort by searching for new identifiers using multiple types of information about metabolites. When multiple types of information were combined, the Chemical Translation System enabled us to update over 3,500 metabolite identifiers in Recon 2. All but approximately 200 identifiers were updated automatically.

Conclusions:

We found that an InChI based application such as the Chemical Translation System was better suited to the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We identified several features, however, that could be added to such an application in order to tailor it to this task.

Abstract

Background:

Correctness of structures and associated metadata within public and commercial chemical databases
greatly impacts drug discovery research activities such as quantitative structure–property relationships modelling and compound novelty checking. MOL files, SMILES notations, IUPAC names, and InChI strings are ubiquitous file formats and systematic identifiers for chemical structures. While interchangeable for many cheminformatics purposes there have been no studies on the inconsistency of these structure identifiers due to various approaches for data integration, including the use of different software and different rules for structure standardisation. We have investigated the consistency of systematic identifiers of small molecules within and between some of the commonly used chemical resources, with and without structure standardisation.

Results:

The consistency between systematic chemical identifiers and their corresponding MOL representation varies greatly between data sources (37.2%-98.5%). We observed the lowest overall consistency for MOL-IUPAC names. Disregarding stereochemistry increases the consistency (84.8% to 99.9%). A wide variation in consistency also exists between MOL representations of compounds linked via cross-references (25.8% to 93.7%). Removing stereochemistry improved the consistency (47.6% to 95.6%).

Conclusions:

We have shown that considerable inconsistency exists in structural representation and systematic chemical identifiers within and between databases. This can have a great influence especially when merging data and if systematic identifiers are used as a key index for structure integration or cross-querying several databases. Regenerating systematic identifiers starting from their MOL representation and applying well-defined and documented chemistry standardisation rules to all compounds prior to creating them can dramatically increase internal consistency.

Abstract

Background:

There are two line notations of chemical structures that have established themselves in the field: the SMILES string and the InChI string. The InChI aims to provide a unique, or canonical, identifier for chemical structures, while SMILES strings are widely used for storage and interchange of chemical structures, but no standard exists to generate a canonical SMILES string.

Results:

I describe how to use the InChI canonicalisation to derive a canonical SMILES string in a straightforward way, either incorporating the InChI normalisations (Inchified SMILES) or not (Universal SMILES). This is the first description of a method to generate canonical SMILES that takes stereochemistry into account. When tested on the 1.1 m compounds in the ChEMBL database, and a 1 m compound subset of the PubChem Substance database, no canonicalisation failures were found with Inchified SMILES. Using Universal SMILES, 99.79% of the ChEMBL database was canonicalised successfully and 99.77% of the PubChem subset.

Conclusions:

The InChI canonicalisation algorithm can successfully be used as the basis for a common standard for canonical SMILES. While challenges remain – such as the development of a standard aromatic model for SMILES – the ability to create the same SMILES using different toolkits will mean that for the first time it will be possible to easily compare the chemical models used by different toolkits.

Abstract

Motivation:

Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools.

Results:

We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run.

Availability:

We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component.

Contact:[email protected]

Abstract

Abstract

The International Chemical Identifier (InChI) has had a dramatic impact on providing a means by which to
deduplicate, validate and link together chemical compounds and related information across databases. Its influence
has been especially valuable as the internet has exploded in terms of the amount of chemistry related information
available online. This thematic issue aggregates a number of contributions demonstrating the value of InChI as an
enabling technology in the world of cheminformatics and its continuing value for linking chemistry data.

Abstract

While chemical databases can be queried using the InChI string and InChIKey (IK) the latter was designed for open-web searching. It is becoming increasingly effective for this since more sources enhance crawling of their websites by the Googlebot and consequent IK indexing. Searchers who use Google as an adjunct to database access may be less familiar with the advantages of using the IK as explored in this review. As an example, the IK for atorvastatin retrieves ~200 low-redundancy links from a Google search in 0.3 of a second. These include most major databases and a very low false-positive rate. Results encompass less familiar but potentially useful sources and can be extended to isomer capture by using just the skeleton layer of the IK. Google Advanced Search can be used to filter large result sets. Image searching with the IK is also effective and complementary to open-web queries. Results can be particularly useful for less-common structures as exemplified by a major metabolite of atorvastatin giving only three hits. Testing also demonstrated document-to-document and document-to-database joins via structure matching. The necessary generation of an IK from chemical names can be accomplished using open tools and resources for patents, papers, abstracts or other text sources. Active global sharing of local IK-linked information can be accomplished via surfacing in open laboratory notebooks, blogs, Twitter, figshare and other routes. While information-rich chemistry (e.g. approved drugs) can exhibit swamping and redundancy effects, the much smaller IK result sets for link-poor structures become a transformative first-pass option. The IK indexing has therefore turned Google into a de-facto open global chemical information hub by merging links to most significant sources, including over 50 million PubChem and ChemSpider records. The simplicity, specificity and speed of matching make it a useful option for biologists or others less familiar with chemical searching. However, compared to rigorously maintained major databases, users need to be circumspect about the consistency of Google results and provenance of retrieved links. In addition, community engagement may be necessary to ameliorate possible future degradation of utility.

Abstract

UniChem is a low-maintenance, fast and freely available compound identifier mapping service, recently made available on the Internet. Until now, the criterion of molecular equivalence within UniChem has been on the basis of complete identity between Standard InChIs. However, a limitation of this approach is that stereoisomers, isotopes and salts of otherwise identical molecules are not considered as related. Here, we describe how we have exploited the layered structural representation of the Standard InChI to create new functionality within UniChem that integrates these related
molecular forms. The service, called ‘Connectivity Search’ allows molecules to be first matched on the basis of complete identity between the connectivity layer of their corresponding Standard InChIs, and the remaining layers then compared to highlight stereochemical and isotopic differences. Parsing of Standard InChI sub-layers permits mixtures and salts to also be included in this integration process. Implementation of these enhancements required simple modifications to the schema, loader and web application, but none of which have changed the original UniChem functionality or services. The scope of queries may be varied using a variety of easily configurable options, and the output is annotated to assist the user to filter, sort and understand the difference between query and retrieved structures. A RESTful web service output may be easily processed programmatically to allow developers to present the data in whatever form they believe their users will require, or to define their own level of molecular equivalence for their resource, albeit within the constraint of identical connectivity.

Abstract

Background: There are many databases of small molecules focused on different aspects of research and its applications. Some tasks may require integration of information from various databases. However, determining which entries from different databases represent the same compound is not straightforward. Integration can be based, for example, on automatically generated cross-reference links between entries. Another approach is to use the manually curated links stored directly in databases. This study employs well-established InChI identifiers to measure the consistency and completeness of the manually curated links by comparing them with the automatically generated ones.

Results: We used two different tools to generate InChI identifiers and observed some ambiguities in their outputs. In part, these ambiguities were caused by indistinctness in interpretation of the structural data used. InChI identifiers were used successfully to find duplicate entries in databases. We found that the InChI inconsistencies in the manually curated links are very high (28.85% in the worst case). Even using a weaker definition of consistency, the measured values were very high in general. The completeness of the manually curated links was also very poor (only 93.8% in the best case) compared with that of the automatically generated links.

Conclusions: We observed several problems with the InChI tools and the files used as their inputs. There are large gaps in the consistency and completeness of manually curated links if they are measured using InChI identifiers. However, inconsistency can be caused both by errors in manually curated links and the inherent limitations of the InChI method.

Abstract

Abstract

IUPAC Standards Online is a database built from IUPAC’s (The International Union of Pure and Applied Chemistry) standards and recommendations, which are extracted from the journal Pure and Applied Chemistry (PAC).

The International Union of Pure and Applied Chemistry (IUPAC) is the organization responsible for setting the standards in chemistry that are internationally binding for scientists in industry and academia, patent lawyers, toxicologists, environmental scientists, legislation, etc. “Standards” are definitions of terms, standard values, procedures, rules for naming compounds and materials, names and properties of elements in the periodic table, and many more.

The database will be the only product that provides for the quick and easy search and retrieval of IUPAC’s standards and recommendations which until now have remained unsorted within the huge Pure and Applied Chemistry archive.

Covered topics:

Analytical Chemistry
Biochemistry
Chemical Safety
Data Management
Education
Environmental Chemistry
Inorganic Chemistry
Materials
Medicinal Chemistry
Nomenclature and Terminology
Nuclear Chemistry
Organic Chemistry
Physical Chemistry
Theoretical & Computational Chemistry
Toxicology

Abstract

Learn about technology that solves the issue of interpreting 3D stereochemical information implied in 2D structure representations.

Abstract

The IUPAC International Chemical Identifier (InChI) is a non-proprietary, machine-readable chemical structure representation format enabling electronic searching, and interlinking and combining, of chemical information from different sources. It was developed from 2001 onwards at the U.S. National Institute of Standards and Technology under the auspices of IUPAC’s Chemical Identifier project. Since 2009, the InChI Trust, a consortium of (mostly) publishers and software developers, has taken over responsibility for funding and oversight of InChI maintenance and development. Funding and responsibility for scientific aspects of InChI development remain with the IUPAC Division VIII (Chemical Nomenclature and Structure Representation) and InChI Subcommittee.

Project for Cheminformatics Fall 2012. Part 2/2. Presentation on encodings, SMILES and InChI.

Abstract

Abstract

Abstract

Optimal descriptors calculated with International Chemical Identifier (InChI) have been used to construct one-variable model of the solubility of fullerene C₆₀ in organic solvents . Attempts to calculate the model for three splits into training and test sets gave stable results.

This presentation is a part of Google Tech Talks which was added to the GoogleTalksArchive on August 22, 2006. The original presentation date took place on November 2, 2006.

ABSTRACT (Imported From YouTube Source)

The central token of information in Chemistry is a chemical substance, an entity that can often be represented as a well-defined chemical structure. With InChI we have a means of representing this entity as a unique string of characters, which is otherwise represented by various of 2-D and 3-D chemical drawings, ‘connection tables’ and synonyms. InChI therefore represents a discrete physical entity, to which is associated as array of chemical properties and data. NIST has long been involved in disseminating chemical reference data associated with such discrete substances. A InChI is therefore the key index to this data. Many other types of data and information are also naturally tied to it, including biological information, commercial availability, toxicity, drug effectiveness and so forth. Because of the diversity of properties and interactions of a chemical substance, effective location of chemical information generally requires further qualifiers, which may be represented coarsely as a key word, but more precisely using a controlled vocabulary. There are no simple separations between information sought by difference disciplines and for different objectives. However, reference data may be organized according the disciplines most directly involved in making the measurements: -isolated substance – mass, infrared, NMR, spectra; physical properties -substance in the context of others – solubility, affinity, .. -properties of a mixture containing the substance The desired data can be a number, vector or image, usually associated with dimensions and links to source information. In some cases, this information is typically converted to a curve or diagram for use by an expert and may be further processed by specialized software. In other cases, a single numerical values is the target. Also, some complexities of structure that must be dealt with in practical search is represented in InChI, but must be decoded for use in searching.

Capturing mixture composition: an open machine-readable format for representing mixed substances

Alex M. Clark, Leah R. McEwen, Peter Gedeck & Barry A. Bunin
Journal of Cheminformatics volume 11, Article number: 33 (2019)

Abstract: We describe a file format that is designed to represent mixtures of compounds in a way that is fully machine readable. This Mixfile format is intended to fill the same role for substances that are composed of multiple components as the venerable Molfile does for specifying individual structures. This much needed datastructure is intended to replace current practices for communicating information about mixtures, which usually relies on human-readable text descriptions, drawing several species within a single molecular diagram, or mutually incompatible ad hoc solutions. We describe an open source software application for editing mixture files, which can also be used as web-ready tools for manipulating the file format. We also present a corpus of mixture examples, which we have extracted from collections of text-based descriptions. Furthermore, we present an early look at the proposed IUPAC Mixtures InChI specification, instances of which can be automatically generated using the Mixfile format as a precursor.

This RDKit InChI Calculation with Jupyter Notebook tutorial is useful to teach the basics of how to interact with InChI using a cheminformatics toolkit in a Jupyter Notebook. The notebook has the following learning objectives:

1. Setup RDKit with a Jupyter Notebook
2. Construct a molecule (RDKit molecular object) from a SMILES string
3. Display molecule images
4. Calculate an InChI for a molecule
5. Calculate InChIs for a list of molecules

This document contains a brief intro to InChI suitable for undergraduate students and two exercises, with answer keys. The first assignment asks about the information encoded in a sample InChI. The last question in this assignment asks students to use the InChI Key as a search term – this will be a lot easier to do if this information is available digitally so that students can simply copy and paste the InChI key rather than typing it by hand into wikipedia.

The second exercise asks students to draw several simple organic compounds with an appropriate computer application and generate the InChI and InChI key. Most commonly used structure drawing programs will readily generate the InChI and InChI key for a structure. In addition there are a number of online services that have a structure drawing application that will generate an InChI or InChI key. Grading this exercise will be much easier if done digitally.

Both exercises were written with the intention that students would complete them on line using a Learning Management System (LMS) such as Blackboard, Canvas, Moodle, etc., where the students would copy and paste an appropriate InChI or InChI KEY into a text box, which the LMS would compare with the correct answer which was submitted by the instructor.

Poster presented by Steve Heller, project director at the Mainz cheminformatics meeting.  Nov 2018

Mainz-poster-final-11-18

Web and high-resolution formats of an InChI infographic.  Nov 2018

HRM_A55856_InChi-trust_infographic

Web_A55856_InChi-trust_infographic

HR_A55856_InChi-trust_infographic

This is a collection of Matlab scripts for working with InChIKeys: IKextract, IKfreqFH, IKstring, and IKmusic

IKextract, InChIKey Extract, can extract InChIKeys from chemical Structure data files (SDFs). This script was successfully used to extract over 90 million InChIKeys (unique chemical identifiers) from over 5000 PubChem SD files. Users can also extract other data from SDFs by specifying the desired SD tag.

IKfreqFH, InChIKey frequency of first hash block, extracts the first hash block of InChIKeys and sorts them by frequency. Such a method is useful for analyzing the variety of chemical connectivity in large datasets.

IKstring, InChIKey String, allows for searching for strings within InChIKeys. I use it to search the > 90 million InChIKeys in PubChem.

IKmusic, InChIKey music, creates music from InChIKeys. A unique song is created for each InChIKey (i.e. every unique chemical substance has a different song!)

This submission shows you how to create a smart spreadsheet with Google Sheets that links an IUPAC name to a chemical’s PubChem landing page. You may click here to get a copy of this sheet.  This particular sheet uses the Centre for Molecular Informatics OPSIN (Open Parser for Systematic IUPAC nomenclature) web service to convert the name to an InChI key, which is then appended to a hyperlink to PubChem.   You will note that some of the names do not work and this is because those names in the sample sheet are incorrect names.  If you paste those names directly into the OPSIN web service, it will tell you were an error in parsing the name occurred.

The following video shows you how to create this  Google Sheet and below it is the instructions and code needed. This application takes advantage of the canonical nature of the InChI and its key, and the fact that the key allows you to communicate over the web.

Step 1: Paste your IUPAC names into a column of your spread sheet

Step 2: Convert IUPAC name to Standard InChI key
type the following script into the top cell of the column you want to place your keys into, and hit enter”

=IMPORTDATA(“http://opsin.ch.cam.ac.uk/opsin/”&[SPREADSHEET CELL WITH IUPAC NAME]&“.stdinchikey”)

• the ampersand(&)concatenates the cell content to the URL
• the ampersand must be surrounded by quotation marks
• the URL must be in quotation marks

Click on the black box in the bottom right corner of cell and drag down, converting the entire column of names to keys.

Step 3: Hyperlink the key to PubChem
Type the following script into the top cell of the column you want to place your links into, and hit enter”n

=HYPERLINK(“https://pubchem.ncbi.nlm.nih.gov/compound/”&[SPREADSHEET CELL WITH INCHIKEY]&“”)

• the ampersand (&) concatenates the cell content to the URL
• the ampersand must be surrounded by quotation marks
• the URL must be in quotation marks

NOTE, these are dynamic cells – And will be recalculated everytime you open the page, or change the chemical name.  If you want them to be static, you can copy the block of cells, and paste to another location as text.

You can also download the sheet as an Excel Spreadsheet, but the downloaded sheet will not be dynamic.  It will be linked, but will not change if you change the IUPAC name.